MS-CONNECT

Similar to the field of genomics, the field of connectomics holds enormous value for to revolutionize clinical diagnostics. Among others, the added value of connectomics lies in its ability to connect functional and structural connectivity, whereas traditional measures, like the number of lesions on a traditional MRI hold little explanatory power regarding the patient functioning.

Reorganization of brain connections is a common characteristic of many brain diseases, including Multiple Sclerosis (MS). Current diagnosis of brain diseases relies on behavioural and neurophysiological testing in combination with structural brain imaging, using e.g. CT and MRI.  These readouts alone are insufficient for clinical decision making, as they are not able to detect changes in brain connectivity. 

MS-CONNECT uses big data analytics to integrate clinical data with imaging data from diffusion tensor imaging (DTI), functional MRI (fMRI) and magnetencephalography (MEG) to create a virtual wiring diagram of all connections in the brain (the connectome). 

The use of connectomics in clinical neuroscience has enormous potential in improving patient and disease characterization, optimizing treatment efficacy and prediction of disease progression. Disruptive research initiatives, like the ‘Human Brain Project’, drive this new era in neuroscience. Still, mainly due to the high complexity and large volumes of data, commercial tools to implement connectomics into clinical practice are lacking.